text: checkpoint_config = dict(interval=5)
  log_config = dict(
      interval=5,
      hooks=[dict(type='TextLoggerHook'),
             dict(type='TensorboardLoggerHook')])
  dist_params = dict(backend='nccl')
  log_level = 'INFO'
  load_from = None
  resume_from = 'work_dirs/wlnet_test_dmey/latest.pth'
  workflow = [('train', 1)]
  optimizer = dict(type='Adam', lr=0.0001)
  optimizer_config = dict(grad_clip=None)
  lr_config = dict(policy='step', step=[700, 1400])
  total_epochs = 1500
  wavelet_type = 'dmey'
  reconstr_points = 50
  model = dict(
      type='WLNet',
      backbone=dict(
          type='mmdet.ResNet',
          depth=50,
          num_stages=4,
          out_indices=(1, 2, 3),
          frozen_stages=-1,
          norm_cfg=dict(type='BN', requires_grad=True),
          norm_eval=True,
          style='pytorch',
          dcn=dict(type='DCNv2', deform_groups=2, fallback_on_stride=False),
          init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'),
          stage_with_dcn=(False, True, True, True)),
      neck=dict(
          type='mmdet.FPN',
          in_channels=[512, 1024, 2048],
          out_channels=256,
          add_extra_convs='on_output',
          num_outs=3,
          relu_before_extra_convs=True,
          act_cfg=None),
      bbox_head=dict(
          type='WLHead',
          wavelet_type='dmey',
          in_channels=256,
          scales=(8, 16, 32),
          loss=dict(type='WLLoss_test', wavelet_type='dmey'),
          postprocessor=dict(
              type='WLPostprocessor',
              num_reconstr_points=50,
              wavelet_type='dmey',
              alpha=1.0,
              beta=2.0,
              score_thr=0.3)))
  dataset_type = 'IcdarDataset'
  data_root = '/home/atom/Research_STD/Datasets/mmocr/ctw1500'
  train = dict(
      type='IcdarDataset',
      ann_file=
      '/home/atom/Research_STD/Datasets/mmocr/ctw1500/instances_training.json',
      img_prefix='/home/atom/Research_STD/Datasets/mmocr/ctw1500/imgs',
      pipeline=None)
  test = dict(
      type='IcdarDataset',
      ann_file=
      '/home/atom/Research_STD/Datasets/mmocr/ctw1500/instances_test.json',
      img_prefix='/home/atom/Research_STD/Datasets/mmocr/ctw1500/imgs',
      pipeline=None)
  train_list = [
      dict(
          type='IcdarDataset',
          ann_file=
          '/home/atom/Research_STD/Datasets/mmocr/ctw1500/instances_training.json',
          img_prefix='/home/atom/Research_STD/Datasets/mmocr/ctw1500/imgs',
          pipeline=None)
  ]
  test_list = [
      dict(
          type='IcdarDataset',
          ann_file=
          '/home/atom/Research_STD/Datasets/mmocr/ctw1500/instances_test.json',
          img_prefix='/home/atom/Research_STD/Datasets/mmocr/ctw1500/imgs',
          pipeline=None)
  ]
  img_norm_cfg = dict(
      mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
  wavelet_type_temp = 'dmey'
  leval_prop_range_icdar2015 = ((0, 0.25), (0.2, 0.65), (0.55, 1.0))
  train_pipeline_icdar2015 = [
      dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
      dict(
          type='LoadTextAnnotations',
          with_bbox=True,
          with_mask=True,
          poly2mask=False),
      dict(
          type='ColorJitter',
          brightness=0.12549019607843137,
          saturation=0.5,
          contrast=0.5),
      dict(
          type='Normalize',
          mean=[123.675, 116.28, 103.53],
          std=[58.395, 57.12, 57.375],
          to_rgb=True),
      dict(type='RandomScaling', size=800, scale=(0.75, 2.5)),
      dict(
          type='RandomCropFlip', crop_ratio=0.5, iter_num=1, min_area_ratio=0.2),
      dict(
          type='RandomCropPolyInstances',
          instance_key='gt_masks',
          crop_ratio=0.8,
          min_side_ratio=0.3),
      dict(
          type='RandomRotatePolyInstances',
          rotate_ratio=0.5,
          max_angle=30,
          pad_with_fixed_color=False),
      dict(type='SquareResizePad', target_size=800, pad_ratio=0.6),
      dict(type='RandomFlip', flip_ratio=0.5, direction='horizontal'),
      dict(type='Pad', size_divisor=32),
      dict(
          type='WLNetTargets',
          wavelet_type='dmey',
          level_proportion_range=((0, 0.25), (0.2, 0.65), (0.55, 1.0))),
      dict(
          type='CustomFormatBundle',
          keys=['p3_maps', 'p4_maps', 'p5_maps'],
          visualize=dict(flag=False, boundary_key=None)),
      dict(type='Collect', keys=['img', 'p3_maps', 'p4_maps', 'p5_maps'])
  ]
  img_scale_icdar2015 = (2260, 2260)
  test_pipeline_icdar2015 = [
      dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
      dict(
          type='MultiScaleFlipAug',
          img_scale=(2260, 2260),
          flip=False,
          transforms=[
              dict(type='Resize', img_scale=(1280, 800), keep_ratio=True),
              dict(
                  type='Normalize',
                  mean=[123.675, 116.28, 103.53],
                  std=[58.395, 57.12, 57.375],
                  to_rgb=True),
              dict(type='Pad', size_divisor=32),
              dict(type='ImageToTensor', keys=['img']),
              dict(type='Collect', keys=['img'])
          ])
  ]
  leval_prop_range_ctw1500 = ((0, 0.25), (0.2, 0.65), (0.55, 1.0))
  train_pipeline_ctw1500 = [
      dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
      dict(
          type='LoadTextAnnotations',
          with_bbox=True,
          with_mask=True,
          poly2mask=False),
      dict(
          type='ColorJitter',
          brightness=0.12549019607843137,
          saturation=0.5,
          contrast=0.5),
      dict(
          type='Normalize',
          mean=[123.675, 116.28, 103.53],
          std=[58.395, 57.12, 57.375],
          to_rgb=True),
      dict(type='RandomScaling', size=800, scale=(0.75, 2.5)),
      dict(
          type='RandomCropFlip', crop_ratio=0.5, iter_num=1, min_area_ratio=0.2),
      dict(
          type='RandomCropPolyInstances',
          instance_key='gt_masks',
          crop_ratio=0.8,
          min_side_ratio=0.3),
      dict(
          type='RandomRotatePolyInstances',
          rotate_ratio=0.5,
          max_angle=30,
          pad_with_fixed_color=False),
      dict(type='SquareResizePad', target_size=800, pad_ratio=0.6),
      dict(type='RandomFlip', flip_ratio=0.5, direction='horizontal'),
      dict(type='Pad', size_divisor=32),
      dict(
          type='WLNetTargets',
          wavelet_type='dmey',
          level_proportion_range=((0, 0.25), (0.2, 0.65), (0.55, 1.0))),
      dict(
          type='CustomFormatBundle',
          keys=['p3_maps', 'p4_maps', 'p5_maps'],
          visualize=dict(flag=False, boundary_key=None)),
      dict(type='Collect', keys=['img', 'p3_maps', 'p4_maps', 'p5_maps'])
  ]
  img_scale_ctw1500 = (1080, 1080)
  test_pipeline_ctw1500 = [
      dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
      dict(
          type='MultiScaleFlipAug',
          img_scale=(1080, 1080),
          flip=False,
          transforms=[
              dict(type='Resize', img_scale=(1280, 800), keep_ratio=True),
              dict(
                  type='Normalize',
                  mean=[123.675, 116.28, 103.53],
                  std=[58.395, 57.12, 57.375],
                  to_rgb=True),
              dict(type='Pad', size_divisor=32),
              dict(type='ImageToTensor', keys=['img']),
              dict(type='Collect', keys=['img'])
          ])
  ]
  leval_prop_range_totaltext = ((0, 0.25), (0.2, 0.65), (0.55, 1.0))
  train_pipeline_totaltext = [
      dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
      dict(
          type='LoadTextAnnotations',
          with_bbox=True,
          with_mask=True,
          poly2mask=False),
      dict(
          type='ColorJitter',
          brightness=0.12549019607843137,
          saturation=0.5,
          contrast=0.5),
      dict(
          type='Normalize',
          mean=[123.675, 116.28, 103.53],
          std=[58.395, 57.12, 57.375],
          to_rgb=True),
      dict(type='RandomScaling', size=800, scale=(0.75, 2.5)),
      dict(
          type='RandomCropFlip', crop_ratio=0.5, iter_num=1, min_area_ratio=0.2),
      dict(
          type='RandomCropPolyInstances',
          instance_key='gt_masks',
          crop_ratio=0.8,
          min_side_ratio=0.3),
      dict(
          type='RandomRotatePolyInstances',
          rotate_ratio=0.5,
          max_angle=30,
          pad_with_fixed_color=False),
      dict(type='SquareResizePad', target_size=800, pad_ratio=0.6),
      dict(type='RandomFlip', flip_ratio=0.5, direction='horizontal'),
      dict(type='Pad', size_divisor=32),
      dict(
          type='WLNetTargets',
          wavelet_type='dmey',
          level_proportion_range=((0, 0.25), (0.2, 0.65), (0.55, 1.0))),
      dict(
          type='CustomFormatBundle',
          keys=['p3_maps', 'p4_maps', 'p5_maps'],
          visualize=dict(flag=False, boundary_key=None)),
      dict(type='Collect', keys=['img', 'p3_maps', 'p4_maps', 'p5_maps'])
  ]
  img_scale_totaltext = (1280, 1280)
  test_pipeline_totaltext = [
      dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
      dict(
          type='MultiScaleFlipAug',
          img_scale=(1280, 1280),
          flip=False,
          transforms=[
              dict(type='Resize', img_scale=(1280, 800), keep_ratio=True),
              dict(
                  type='Normalize',
                  mean=[123.675, 116.28, 103.53],
                  std=[58.395, 57.12, 57.375],
                  to_rgb=True),
              dict(type='Pad', size_divisor=32),
              dict(type='ImageToTensor', keys=['img']),
              dict(type='Collect', keys=['img'])
          ])
  ]
  data = dict(
      samples_per_gpu=8,
      workers_per_gpu=8,
      val_dataloader=dict(samples_per_gpu=1),
      test_dataloader=dict(samples_per_gpu=1),
      train=dict(
          type='UniformConcatDataset',
          datasets=[
              dict(
                  type='IcdarDataset',
                  ann_file=
                  '/home/atom/Research_STD/Datasets/mmocr/ctw1500/instances_training.json',
                  img_prefix=
                  '/home/atom/Research_STD/Datasets/mmocr/ctw1500/imgs',
                  pipeline=None)
          ],
          pipeline=[
              dict(
                  type='LoadImageFromFile',
                  color_type='color_ignore_orientation'),
              dict(
                  type='LoadTextAnnotations',
                  with_bbox=True,
                  with_mask=True,
                  poly2mask=False),
              dict(
                  type='ColorJitter',
                  brightness=0.12549019607843137,
                  saturation=0.5,
                  contrast=0.5),
              dict(
                  type='Normalize',
                  mean=[123.675, 116.28, 103.53],
                  std=[58.395, 57.12, 57.375],
                  to_rgb=True),
              dict(type='RandomScaling', size=800, scale=(0.75, 2.5)),
              dict(
                  type='RandomCropFlip',
                  crop_ratio=0.5,
                  iter_num=1,
                  min_area_ratio=0.2),
              dict(
                  type='RandomCropPolyInstances',
                  instance_key='gt_masks',
                  crop_ratio=0.8,
                  min_side_ratio=0.3),
              dict(
                  type='RandomRotatePolyInstances',
                  rotate_ratio=0.5,
                  max_angle=30,
                  pad_with_fixed_color=False),
              dict(type='SquareResizePad', target_size=800, pad_ratio=0.6),
              dict(type='RandomFlip', flip_ratio=0.5, direction='horizontal'),
              dict(type='Pad', size_divisor=32),
              dict(
                  type='WLNetTargets',
                  wavelet_type='dmey',
                  level_proportion_range=((0, 0.25), (0.2, 0.65), (0.55, 1.0))),
              dict(
                  type='CustomFormatBundle',
                  keys=['p3_maps', 'p4_maps', 'p5_maps'],
                  visualize=dict(flag=False, boundary_key=None)),
              dict(
                  type='Collect', keys=['img', 'p3_maps', 'p4_maps', 'p5_maps'])
          ]),
      val=dict(
          type='UniformConcatDataset',
          datasets=[
              dict(
                  type='IcdarDataset',
                  ann_file=
                  '/home/atom/Research_STD/Datasets/mmocr/ctw1500/instances_test.json',
                  img_prefix=
                  '/home/atom/Research_STD/Datasets/mmocr/ctw1500/imgs',
                  pipeline=None)
          ],
          pipeline=[
              dict(
                  type='LoadImageFromFile',
                  color_type='color_ignore_orientation'),
              dict(
                  type='MultiScaleFlipAug',
                  img_scale=(1080, 1080),
                  flip=False,
                  transforms=[
                      dict(
                          type='Resize', img_scale=(1280, 800), keep_ratio=True),
                      dict(
                          type='Normalize',
                          mean=[123.675, 116.28, 103.53],
                          std=[58.395, 57.12, 57.375],
                          to_rgb=True),
                      dict(type='Pad', size_divisor=32),
                      dict(type='ImageToTensor', keys=['img']),
                      dict(type='Collect', keys=['img'])
                  ])
          ]),
      test=dict(
          type='UniformConcatDataset',
          datasets=[
              dict(
                  type='IcdarDataset',
                  ann_file=
                  '/home/atom/Research_STD/Datasets/mmocr/ctw1500/instances_test.json',
                  img_prefix=
                  '/home/atom/Research_STD/Datasets/mmocr/ctw1500/imgs',
                  pipeline=None)
          ],
          pipeline=[
              dict(
                  type='LoadImageFromFile',
                  color_type='color_ignore_orientation'),
              dict(
                  type='MultiScaleFlipAug',
                  img_scale=(1080, 1080),
                  flip=False,
                  transforms=[
                      dict(
                          type='Resize', img_scale=(1280, 800), keep_ratio=True),
                      dict(
                          type='Normalize',
                          mean=[123.675, 116.28, 103.53],
                          std=[58.395, 57.12, 57.375],
                          to_rgb=True),
                      dict(type='Pad', size_divisor=32),
                      dict(type='ImageToTensor', keys=['img']),
                      dict(type='Collect', keys=['img'])
                  ])
          ]))
  evaluation = dict(interval=5, metric='hmean-iou')
  work_dir = './work_dirs/wlnet_test_dmey'
  gpu_ids = range(0, 1)
  